Combating Antimicrobial Resistance With New-To-Nature Lanthipeptides Created by Genetic Code Expansion
Bibliographic record
Abstract
Due to the rapid emergence of multi-resistant bacterial strains in recent decades, the commercially available effective antibiotics are becoming increasingly limited. On the other hand, widespread antimicrobial peptides (AMPs) such as the lantibiotic nisin has been used worldwide for more than 40 years without the appearance of significant bacterial resistance. Lantibiotics are ribosomally synthesized antimicrobials generated by posttranslational modifications. Their biotechnological production is of particular interest to redesign natural scaffolds improving their pharmaceutical properties, which has great potential for therapeutic use in human medicine and other areas. However, conventional protein engineering methods are limited to 20 canonical amino acids prescribed by the genetic code. Therefore, the expansion of the genetic code as the most advanced approach in Synthetic Biology allows the addition of new amino acid building blocks (non-canonical amino acids, ncAAs) during protein translation. We now have solid proof-of-principle evidence that bioexpression with these novel building blocks enabled lantibiotics with chemical properties transcending those produced by natural evolution. The unique scaffolds with novel structural and functional properties are the result of this bioengineering. Here we will critically examine and evaluate the use of the expanded genetic code and its alternatives in lantibiotics research over the last 7 years. We anticipate that Synthetic Biology, using engineered lantibiotics and even more complex scaffolds will be a promising tool to address an urgent problem of antibiotic resistance, especially in a class of multi-drug resistant microbes known as superbugs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".